A cooperative detection method and system in a UAV common sense integrated network

Abstract

The application belongs to but is not limited to the technical field of unmanned aerial vehicle communication, and discloses a cooperative detection method and system in an unmanned aerial vehicle communication and sensing integrated network, an unmanned aerial vehicle communication and sensing integrated system is constructed, a base station and a detection unmanned aerial vehicle jointly perform a detection task; in a cooperative detection scene, an unmanned aerial vehicle communication and sensing signal transmission and reception signal model is established; according to a maximum likelihood criterion, detection sets of the base station and the unmanned aerial vehicle are subjected to sensing data fusion by using an EKF algorithm; a maximum problem of an achievable communication rate is subjected to mathematical modeling; communication performance is enhanced by jointly optimizing an unmanned aerial vehicle trajectory and a beamforming algorithm to solve an optimization problem of the maximum of the achievable communication rate. The application can solve problems of a black flight unmanned aerial vehicle, such as high mobility, miniaturization and being easily blocked by buildings, and effectively improves sensing accuracy, enhances cooperative sensing distance and enhances detection performance by using communication to fuse sensing information.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of unmanned aerial vehicle communication, and particularly relates to a cooperative detection method and system in an unmanned aerial vehicle communication and sensing integrated network. BACKGROUND

[0002] In recent years, with the development of unmanned aerial vehicle technology, unmanned aerial vehicles have shown great potential in many fields, such as logistics, agriculture, disaster relief and national defense. In complex scenes such as cities, unmanned aerial vehicles are difficult to achieve accurate and rapid detection in high-rise building environments due to their high mobility, small size and easy blocking by buildings. This not only limits the detection ability of high-power radars on long-distance targets, but also makes it difficult to meet the needs of modern countermeasures.

[0003] By using wireless communication to cooperatively network unmanned aerial vehicles and mobile communication networks, and integrating the sensing data of multiple nodes in the network, the cooperative sensing area far exceeds the sensing area of a single radar. However, during the integration of unmanned aerial vehicles and cellular networks, many challenges are faced, such as mobility, interference and degradation of communication quality. Based on this problem, unmanned aerial vehicle and base station cooperative detection has been proven to be an effective method, and through the cooperative work of multiple unmanned aerial vehicles, the overall performance of the system can be significantly improved. In addition, the communication and sensing integration technology uses a unified transceiver and spectrum resource to achieve load saving and spectrum reuse, while providing efficient cooperative detection and sensing data sharing capabilities, supporting positioning, ranging, speed measurement, imaging, detection, identification and other functions, which can effectively alleviate the problem of insufficient detection capability of single radar sensors and improve the overall performance and service capability of the network.

[0004] Currently, the research on unmanned aerial vehicle communication and sensing integrated networks mainly considers using communication to assist sensing, or using sensing to assist communication. This mode mainly emphasizes one-way functional complementation, and less involves the positive feedback mechanism between the two. SUMMARY

[0005] In view of the problem that unmanned aerial vehicles are difficult to detect due to high mobility, small size and easy blocking by buildings, the application proposes a cooperative detection method and system in an unmanned aerial vehicle communication and sensing integrated network, which reasonably designs an sensing data fusion algorithm and a joint unmanned aerial vehicle trajectory and beamforming design algorithm.

[0006] The application is implemented as follows: a cooperative detection method in an unmanned aerial vehicle communication and sensing integrated network, comprising the following steps:

[0007] Step 1: Constructing an unmanned aerial vehicle communication and sensing integrated system, the base station and the detection unmanned aerial vehicle jointly perform the detection task; in the cooperative detection scene, a unmanned aerial vehicle communication and sensing signal transmission and reception signal model is established;

[0008] Step two, according to the maximum likelihood criterion, the base station and the detection set of the UAV are fused by the EKF algorithm for sensing data;

[0009] Step three, the maximum problem of the reachable communication rate is mathematically modeled;

[0010] Step four, the optimization problem of the maximum reachable communication rate is solved by jointly optimizing the UAV trajectory and the beamforming algorithm to enhance the communication performance.

[0011] Further, the UAV adopts a sensing-integrated multi-beam signal, and uses a fixed sub-beam to perform a communication function and a scanning sub-beam to perform a sensing function.

[0012] Further, the detection UAV is connected with the ground cellular network, and flies from an initial point to a terminal point within a given time; during the flight, the detection UAV maintains reliable communication with the cellular network and simultaneously monitors K invading UAVs around in real time; the detection UAV always flies at a fixed height H, and the flight time t is in the interval [0, T], which is divided into N = T / Δt equal time slots, and each time slot has a length of Δt; n ∈ [1, 2, …, N] represents the nth time slot; each time slot is set to be small enough, and the position of the detection UAV remains unchanged in the time slot; the flight trajectory of the detection UAV is q u,c [n] = [x u,c [n], y u,c [n], H] T , 1 ≤ n ≤ N.

[0013] Further, the transmission signal of the detection UAV is where s i [n] represents the transmitted information, and w i [n] represents the corresponding beamforming vector; since the detection UAV carries limited energy, the transmission power is limited to

[0014] The received signal is where h c [n] represents the channel vector between the detection UAV and the base station, and z c [n] is an additive white Gaussian noise with a variance of σ ; the first term represents an expected received communication signal, and the second term represents channel interference;

[0015] The signal-to-interference-plus-noise ratio (SINR) of the received communication signal is

[0016] The reachable communication rate of the received communication signal is C com [n] = log2(1 + γ c[n]);

[0017] The transmit beam gain of the sensing function is represented as

[0018]

[0019] wherein denotes the steering vector, θ n denotes the angle of arrival.

[0020] Further, the probe results of the UAV and the base station are represented as S U and S B respectively, wherein S = (Ω, V), denotes the position information, and V = μ denotes the movement state information. The normalized Euclidean distance between the two probe result sets is represented as wherein and denote the maximum Euclidean distance of the position information and the movement state information, respectively.

[0021] Further, the specific operation steps of the EKF sensing data fusion algorithm are as follows:

[0022] A1, initialize an empty set S F ;

[0023] A2, calculate the normalized Euclidean distance between the two probe result sets;

[0024] A3, traverse the two probe result sets, Γ d is a set threshold value, if Ψ i,j ≤ Γ d , perform sensing data fusion and put into the set S F ; otherwise, directly put into the set S F .

[0025] Further, the specific steps of data fusion are as follows:

[0026] A31, state prediction: x n|n-1 = g(x n-1 ) + ω n ;

[0027] A32, prediction covariance matrix calculation:

[0028] A33, Kalman filter gain calculation:

[0029] A34, state update: x n = x n|n-1 + K n (y n - h(x n|n-1 )).

[0030] Further, the UAV needs to fly from the starting point q I to the ending point q F with the maximum flight speed V max , the maximum moving distance between two time slots D m = V max Δt, the flight restriction q u,c [1] = q I , q u,c [N] = q F , and ||q u,c [n+1]-q u,c [n]||≤D m .

[0031] Further, the UAV trajectory and the beamforming algorithm are jointly optimized, and the method specifically comprises the following steps:

[0032] A1, initializing the beamforming vector matrix W and the UAV flight trajectory Q, and setting the iteration number l = 0;

[0033] A2, for the given UAV flight trajectory Q, the original optimization problem is converted into an optimization problem with only the beamforming vector matrix W as an optimization variable, a non-convex problem is converted into a convex optimization problem by using a semi-definite relaxation technique, and thus the optimal beamforming vector matrix W l+1 can be solved by using a convex optimization tool package;

[0034] A3, for the given beamforming vector matrix W l+1 , the original optimization problem is converted into an optimization problem with only the UAV flight trajectory Q as an optimization variable, which is a typical non-convex optimization problem, a non-convex problem is converted into a convex optimization problem by using a continuous convex approximation technique, and thus the optimal power allocation matrix Q l+1 can be solved by using a convex optimization tool package;

[0035] A4, the maximum communication reachable rate of the current iteration is obtained by using the obtained beamforming vector matrix W l+1 and the UAV flight trajectory Q l+1 ; Let γ0 be a set threshold value, if γ ≥ γ0, the iteration number l = l + 1 is updated, and the step A2 is returned; otherwise, the process is directly ended.

[0036] Another purpose of the present application is to provide a cooperative detection system in a UAV C2I network, which comprises a detection UAV and a base station.

[0037] The base station and the detection unmanned aerial vehicle jointly perform a detection task; in the cooperative detection scene, a signal transmission and reception model of unmanned aerial vehicle communication and sensing is established;

[0038] According to the maximum likelihood criterion, the detection set of the base station and the unmanned aerial vehicle is fused by using an EKF algorithm;

[0039] A mathematical model of the maximum reachable communication rate is established;

[0040] The communication performance is enhanced by jointly optimizing the unmanned aerial vehicle trajectory and the beamforming algorithm, and the optimization problem of the maximum reachable communication rate is solved.

[0041] Through data simulation, the effectiveness of the algorithm in the integrated design of unmanned aerial vehicle communication and sensing is verified. The simulation results show that, compared with the traditional integrated communication and sensing which focuses on one-way functional complementation, the present application can significantly improve the sensing capability while ensuring the performance of the communication system, and the communication and sensing complement each other, which provides strong support for the safe flight of unmanned aerial vehicles in complex urban environments. In addition, the implementation of the algorithm helps to promote the development of unmanned aerial vehicle communication technology and lay a foundation for the construction of future smart cities.

[0042] In combination with the above technical solutions and solved technical problems, the technical solution to be protected by the present application has the following advantages and positive effects:

[0043] First, in the traditional unmanned aerial vehicle communication system, communication and sensing are usually completed by independent modules or devices, resulting in low data transmission efficiency, slow system response speed, difficulty in coping with multi-node network topology, delayed information sharing and decision-making. How to design a method suitable for multi-node networking cooperative detection, while realizing the dynamic optimization and scheduling of communication, calculation and power resources in the unmanned aerial vehicle communication and sensing cooperative system, is the key to technical research.

[0044] The present application proposes a cooperative detection method and system in an integrated unmanned aerial vehicle communication and sensing network, which considers the bidirectional dynamic cooperation between communication and sensing. In this mechanism, communication and sensing complement each other, and the sensing accuracy is effectively improved by using communication to fuse sensing information, the cooperative sensing distance is improved, and the enhanced detection performance can further enhance the communication performance by jointly designing the unmanned aerial vehicle trajectory and the beamforming design, which fills the existing research gap.

[0045] This invention discloses a collaborative detection method and system in an integrated sensing network for unmanned aerial vehicles (UAVs). This method addresses the challenge of achieving accurate and rapid detection in environments with high-rise buildings, where traditional radar-based detection methods struggle due to the high maneuverability, miniaturization, and susceptibility to building obstruction of UAVs. Furthermore, it considers bidirectional dynamic collaboration between sensing elements, where communication and perception complement each other. By fusing sensing information through communication, it effectively improves sensing accuracy and extends collaborative sensing range. Enhanced detection performance can be further improved through joint design of UAV trajectories and beamforming techniques to enhance communication performance. The method of this invention has the following advantages:

[0046] 1. The UAV employs an integrated multi-beam signaling system, utilizing fixed sub-beams for communication and scanning sub-beams for sensing. This enhances system flexibility, enabling the UAV to adapt its communication and sensing strategies to different mission scenarios, thereby improving mission completion efficiency.

[0047] 2. The EKF algorithm is designed to fuse detection information from the detection drone and the base station, which can effectively improve detection accuracy and increase detection range.

[0048] 3. Joint design of UAV trajectory and beamforming design further enhances communication performance. An iterative algorithm is proposed to solve the optimization problem of maximizing the achievable communication rate. This not only improves the stability of the communication link, but also maximizes the communication rate, thereby improving the communication efficiency of the entire network.

[0049] 4. The proposed algorithm is applicable to UAV integrated sensing networks with different performance requirements and flight times, ensuring the stability and reliability of the system and significantly expanding the application fields of UAV communication.

[0050] Second, the expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0051] The technical solution of this invention enables precise perception and real-time tracking of low-altitude drones, effectively solving the safety monitoring challenges of unauthorized low-altitude drone flights and providing strong technical support for government regulatory departments. Simultaneously, by providing efficient technical monitoring methods, it ensures the safety and reliability of low-altitude flight missions, thereby promoting the widespread application of drones in fields such as express delivery and logistics, geographic surveying, urban management, and emergency rescue, and driving the rapid development of the low-altitude economy. Furthermore, for telecommunications operators, the application of integrated sensing technology is expected to provide new business growth points and expand market space in the low-altitude economy sector.

[0052] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0053] This invention utilizes 5G-A sensing technology, through continuous networking and collaborative detection capabilities between a sensing base station and a detection drone, to achieve functions such as multi-target detection, continuous trajectory tracking, and electronic fence intrusion warning for low-altitude drones. The application of this technology not only meets the application needs of low-altitude security and traffic management but also provides technical support for the steady development of the low-altitude economy. Previously, simulation data verified its full-domain, high-precision low-altitude sensing capabilities. This technological breakthrough promotes and guides the further development of sensing technology in the low-altitude economy, fills this technological gap, and lays a solid foundation for the rapid development of the low-altitude economy.

[0054] (3) The technical solution of the present invention solves a technical problem that people have long wanted to solve but have never been able to solve successfully:

[0055] In complex urban environments, drones, due to their high maneuverability, small size, and susceptibility to building obstruction, make it difficult for traditional radar-based detection methods to achieve accurate and rapid detection in environments with tall buildings. This not only limits the detection capability of high-power radar against long-range targets but also fails to meet the demands of modern countermeasures. Therefore, more advanced technologies are urgently needed to address this challenge. Currently, 5G networks are widely deployed. With their high bandwidth and large antenna array technology, they can provide not only high-precision sensing capabilities but also low-cost, all-weather ubiquitous sensing by reusing base station sites, equipment, and spectrum resources. This provides a new solution for drone detection in complex urban environments. Integrated sensing and computing systems can sense and track drones in real time, thereby establishing electronic fences to prevent drones from intruding into specific areas. This systematic and networked sensing capability not only improves the safety and reliability of drone flights but also provides strong technical support for public safety in urban environments.

[0056] (4) The technical solution of the present invention overcomes technical bias:

[0057] The technical solution of this invention overcomes technical bias to a certain extent. Traditional radar detection technology suffers from poor identification capabilities when facing "low, slow, and small" targets, while the integrated sensing technology, by integrating wireless communication and sensing functions, achieves accurate perception and real-time tracking of low-altitude UAVs. This technical solution not only improves detection accuracy but also further enhances the reliability and accuracy of the system through the integrated application of technologies such as multi-node collaboration, data fusion, and multi-dimensional resource optimization, providing strong support for low-altitude security.

[0058] Third, this invention proposes a collaborative detection method in an integrated UAV sensing network. This method primarily achieves the integration of communication and sensing functions in UAVs through the collaborative work of a base station and the detecting UAV. In this system, by establishing a transmission and reception model for UAV communication and sensing signals, communication and sensing tasks can be performed simultaneously. Furthermore, the extended Kalman filter (EKF) algorithm is used for sensing data fusion to improve detection accuracy. Joint optimization of the UAV trajectory and beamforming algorithm further enhances communication performance and increases the achievable communication rate.

[0059] This invention employs a multi-beam signal design for the communication and sensing functions of a UAV. It utilizes a fixed sub-beam for communication and a scanning sub-beam for sensing, ensuring reliable communication throughout the UAV's flight and enabling real-time monitoring of the surrounding environment. This multi-beam signal design not only guarantees communication reliability but also significantly improves sensing accuracy, making it particularly suitable for complex and dynamic collaborative detection scenarios.

[0060] In terms of technical implementation, the EKF sensing data fusion algorithm and semi-definite relaxation and continuous convex approximation techniques are used to jointly optimize the UAV's trajectory and beamforming algorithms, solving the non-convex optimization problem of maximizing the achievable communication rate. This method ensures the optimal communication rate of the UAV under limited energy conditions while effectively improving the system's sensing accuracy and reliability.

[0061] This method represents a significant technological advancement based on existing technologies. It not only achieves innovative breakthroughs in the coordination of UAV communication and sensing functions, but also demonstrates high practicality and broad application prospects in real-world industrial applications.

[0062] Fourth, by introducing key parameters, algorithms, and mathematical models, this invention solves several key technical problems existing in the current technology of UAV integrated sensing networks, and achieves significant technological progress.

[0063] First, the communication and sensing signal transmission and reception model of the UAV in this invention achieves sensing data fusion from multiple detection results by combining the maximum likelihood criterion with the extended Kalman filter (EKF) algorithm. The EKF algorithm can effectively handle the state estimation problem of nonlinear systems, thus significantly improving the sensing accuracy of the UAV in complex environments. By calculating the normalized Euclidean distance and fusing and filtering the detection results, the problems of difficulty and insufficient accuracy in multi-source sensing data fusion in existing technologies are solved.

[0064] Secondly, this invention addresses the problem of maximizing achievable communication rates by employing a mathematical modeling method. By modeling the communication channel of the UAV and combining it with a beamforming algorithm, communication performance is optimized. In existing technologies, communication rates are often constrained by the limited energy of UAVs and the complex communication environment. This invention, by jointly optimizing the UAV's trajectory and beamforming algorithm, and applying semidefinite relaxation techniques and continuous convex approximation techniques, transforms the original non-convex optimization problem into a convex optimization problem, thereby effectively solving for the optimal beamforming matrix and flight trajectory, significantly improving the UAV's communication performance.

[0065] Third, this invention designs a jointly optimized UAV trajectory and beamforming algorithm. By iteratively optimizing the beamforming vector matrix and the UAV flight trajectory, it not only improves the communication rate but also significantly extends the UAV's continuous working capability under limited energy conditions. This effectively solves the energy consumption bottleneck problem that is prevalent in existing technologies.

[0066] In summary, this invention effectively solves the technical challenges of existing technologies in the UAV integrated sensing network by optimizing sensing and communication functions. This includes breakthroughs in multi-source data fusion, communication rate optimization, and energy management, significantly improving the overall performance of the system and promoting the application and development of UAV collaborative detection technology. Attached Figure Description

[0067] Figure 1 This is a flowchart of a collaborative detection method in an integrated sensor network for unmanned aerial vehicles (UAVs) provided in an embodiment of the present invention.

[0068] Figure 2 This is a schematic diagram illustrating the working principle of the integrated inductive multibeam transducer provided in an embodiment of the present invention.

[0069] Figure 3 This is a flowchart of the EKF sensing data fusion algorithm provided in an embodiment of the present invention;

[0070] Figure 4 This is a flowchart of the joint optimization algorithm for UAV trajectory and beamforming provided in an embodiment of the present invention;

[0071] Figure 5 This is a structural diagram of a collaborative detection system in an integrated sensor network for unmanned aerial vehicles (UAVs) provided in an embodiment of the present invention.

[0072] Figure 6 Simulation diagram comparing the performance of the proposed algorithm under different sensing beam gain constraints provided in the embodiments of the present invention with other basic algorithms at achievable communication rates;

[0073] Figure 7 Simulation diagrams comparing the performance of the proposed algorithm at different flight times under achievable communication rates, as provided in embodiments of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0075] This invention provides a collaborative detection method in an integrated UAV sensing network, constructing an integrated UAV sensing system with dual communication and sensing functions. The base station and the detection UAV jointly perform detection tasks. Information sharing is achieved through communication between the base station and the detection UAV, fusing sensing data to effectively improve detection accuracy and extend detection range. Enhanced detection performance can be further improved by jointly designing UAV trajectories and beamforming designs to enhance communication performance, thereby designing an iterative algorithm to further address the optimization problem of maximizing the achievable communication rate. Data simulations verify the effectiveness of the proposed method.

[0076] like Figure 1 As shown in the figure, a cooperative detection method in an integrated sensor network for unmanned aerial vehicles (UAVs) provided by an embodiment of the present invention includes the following steps:

[0077] Step 1: Construct an integrated UAV sensing system, in which the base station and the detection UAV jointly perform detection tasks; in the collaborative detection scenario, establish a model for the transmission and reception of communication and sensing signals by the UAV.

[0078] Step 2: Based on the maximum likelihood criterion, the detection sets of the base station and the UAV are fused using the EKF algorithm to perform perception data fusion;

[0079] Step 3: Mathematically model the problem of maximizing the achievable communication rate;

[0080] Step four: Enhance communication performance by jointly optimizing UAV trajectory and beamforming algorithms to solve the optimization problem of maximizing achievable communication rate.

[0081] like Figure 2 As shown, the transmitting antenna of the detection drone uses a uniform linear array to generate multiple beams, and its transmitted signal can be expressed as follows: Where s i [n] represents the baseband signal. Indicates the transmitted beamforming vector. Represents the phase offset vector, β R Indicates the power allocation factor. and These represent the beamforming vectors of the sensing and communication sub-beams, respectively. Through flexible adjustment... and β RIt can simultaneously meet the needs of communication and sensing, with the fixed sub-beam performing communication functions and the scanning sub-beam performing sensing functions.

[0082] The reconnaissance drone connects to a ground-based cellular network and flies from an initial point to a destination within a given timeframe. During this time, it needs to maintain reliable communication with the cellular network while simultaneously monitoring the surrounding environment in real time. s An intrusion drone is deployed. The drone flies at a fixed altitude H for a time t ∈ [0, T], divided into N = T / Δt equal-length time slots, each with a length of Δt. n ∈ [1, 2, ..., N] represents the nth time slot. Each time slot is set sufficiently small so that the drone's position remains constant within that slot, and its flight trajectory is q. u,c [n] = [x] u,c [n],y u,c [n],H] T ,1≤n≤N.

[0083] Because the detection drone has limited energy carrying capacity, its transmission power is limited to [specific value]. The received signal is Where f0[n] represents the corresponding received signal beamforming vector, h c [n] represents the channel vector between the probe drone and the base station, z c [n] is the variance, which is... Additive white Gaussian noise (CBD) effectively simulates background noise in many real-world physical systems. In communication systems, noise from a source superimposed on the signal at the receiver behaves as additive noise. The white noise characteristic means that the noise power is uniformly distributed across all frequencies. In the formula for the received signal, the first term represents the desired received communication signal, and the second term represents channel interference.

[0084] Therefore, the signal-to-interference-plus-noise ratio (SINR) of the received communication signal is: The achievable communication rate for receiving communication signals is C. com [n] = log2(1+γ) c [n]). The transmit beam gain of this sensing function is expressed as: in Represents the guiding vector, θ n Indicates the angle of arrival.

[0085] The detection results from the drone and the base station are respectively represented as S. U With S B Where S = (Ω, V), Represents the target's location information, r, θ. Let V represent the range, horizontal angle, and pitch angle of the detected target, respectively, and let V = μ represent the target's movement status information. The normalized Euclidean distance between the two sets of detection results is expressed as: in as well as These represent the maximum Euclidean distance between the location information and the movement status information, respectively.

[0086] like Figure 3 As shown, the EKF algorithm is used for sensor data fusion. The specific operation steps are as follows:

[0087] S31. Initialize the empty set S F ;

[0088] S32. Calculate the normalized Euclidean distance between two sets of detection results;

[0089] S33, Traverse the two sets of detection results, Γ d For the set threshold value, if Ψ i,j ≤Γ d Perform sensor data fusion and put it into set S F Otherwise, directly put it into set S. F The specific steps for data fusion are as follows:

[0090] (1) State prediction: x n|n-1 =g(x n-1 )+ω n ;

[0091] (2) Calculation of the predicted covariance matrix:

[0092] (3) Kalman filter gain calculation:

[0093] (4) State update: x n =x n|n-1 +K n (y n -h(x n|n-1 )).

[0094] Where, x n Represents the state variable, y n Let g(x) represent the measurement variable, g(x) be the state transition function, and h(x) be the Jacobian matrix of the measurement function with respect to the state variable. as well as

[0095] The drone needs to start from point q within the specified time. I Fly to the finish line q F The maximum flight speed is V maxThe maximum travel distance between the two time slots is D. m =V max Δt. Flight is limited to q. u,c [1] = q I q u,c [N] = q F , and ||q u,c [n+1]-q u,c [n]||≤D m .

[0096] like Figure 4 As shown, the joint optimization algorithm for UAV trajectory and beamforming follows the specific steps as follows:

[0097] S41. Initialize the beamforming vector matrix W and the UAV flight trajectory Q, and set the iteration number l = 0;

[0098] S42. For a given UAV flight trajectory Q, the original optimization problem is transformed into an optimization problem with only the beamforming vector matrix W as the optimization variable, which can be expressed as:

[0099] Optimization issues:

[0100] Constraints: P s,j (q u,c [n])≥Γ s

[0101]

[0102] At this point, the objective function is non-convex. First, we use a positive semidefinite relaxation technique to transform the non-convex problem into a convex optimization problem. The specific transformation steps are as follows:

[0103] S421, Order and at this time, C is obtained by expanding the first-order Taylor formula. com The lower bound of [n] is

[0104] in, This represents the local value at the m-th iteration. At this point, the objective function has been transformed into a convex function;

[0105] S42, Order The original optimization problem is transformed into:

[0106] Optimization issues:

[0107] Constraints: tr(a) H (q u,c [n])W k a(qu,c [n]))≥Γ s

[0108]

[0109] rank(W k )≤1

[0110] At this point, the optimization problem is a standard positive semidefinite relaxation optimization problem, and the optimal beamforming vector matrix W can be solved using the convex optimization toolkit. l+1 ;

[0111] S43. For a given beamforming vector matrix W l+1 The original optimization problem is transformed into an optimization problem with only the drone's flight trajectory Q as the optimization variable, which can be expressed as:

[0112] Optimization issues:

[0113] Constraints: P s,j (q u,c [n])≥Γ s

[0114] q u,c [1] = q I ,q u,c [N] = q F

[0115] ||q u,c [n+1]-q u,c [n]||≤D m

[0116] This is a typical non-convex optimization problem. It can be transformed into a convex optimization problem by using continuous convex approximation techniques. The specific transformation steps are as follows:

[0117] S431, Order

[0118] and Where F0[n] p,q d represents the p-th row and q-th column of the matrix. c,b The distance between the drone and the base station is represented by C, which is obtained by expanding the first-order Taylor formula. com The lower bound of [n] is in This represents the local value at the m-th iteration. At this point, the objective function has been transformed into a convex function;

[0119] S432, Order and P is obtained by expanding the first-order Taylor formula. s,j (qu,c The lower bound of [n]) is At this point, the optimization objective constraint has been transformed into a convex function;

[0120] S433. To enhance the approximation accuracy, the confidence interval is set as follows: ||q (m+1) [n]-q (m) [n]||≤r (m) r (m) This represents the confidence region for the m-th iteration;

[0121] S434. At this point, the original optimization problem is transformed into:

[0122] Optimization issues:

[0123] Constraints:

[0124] ||q (m+1) [n]-q (m) [n]||≤r (m)

[0125] This optimization problem is a convex optimization problem, and therefore the optimal power allocation matrix Q can be solved using a convex optimization toolkit. l+1 ;

[0126] S44. Using the already obtained beamforming vector matrix W l+1 and drone flight trajectory Q l+1 Find the maximum achievable communication rate in the current iteration. make γ0 is the set threshold value. If γ≥γ0, update the iteration count l=l+1 and return to step A2; otherwise, end directly.

[0127] like Figure 5 As shown in the figure, the cooperative detection system in the UAV integrated sensing network provided by the embodiment of the present invention includes a detection UAV and a base station;

[0128] Base stations and detection drones jointly perform detection tasks; in collaborative detection scenarios, a model for drone communication and sensing signal transmission and reception is established;

[0129] Based on the maximum likelihood criterion, the EKF algorithm is used to fuse the sensing data of the base station and the UAV detection sets;

[0130] Mathematical modeling of the problem of maximizing achievable communication rates;

[0131] By jointly optimizing UAV trajectories and beamforming algorithms, communication performance is enhanced, solving the optimization problem of maximizing achievable communication rates.

[0132] The cooperative detection method and system in the integrated sensor network of unmanned aerial vehicles (UAVs) of the present invention has a wide range of applications and potential related products, specifically including the following aspects:

[0133] 1. Urban airspace management and safety monitoring

[0134] Application Areas: This invention can be applied to urban airspace management, particularly for the monitoring and management of unauthorized drones flying in urban areas with numerous high-rise buildings. Through collaborative detection technology, the system can effectively detect and locate illegal drones in complex urban environments, providing technical support for urban airspace security.

[0135] Related products: UAV airspace monitoring system, urban safety management platform, and illegal UAV detection and jamming equipment.

[0136] 2. Smart Cities and Intelligent Transportation Management

[0137] Application Areas: This invention can be applied to smart city and intelligent traffic management systems for real-time monitoring and management of drone traffic flow, ensuring the safe operation of drones. Simultaneously, collaborative detection by drones can enhance the breadth and accuracy of urban monitoring.

[0138] Related products: Intelligent traffic management system, drone traffic monitoring platform, urban air traffic management system.

[0139] 3. Public safety and emergency response

[0140] Application Areas: This invention can be applied to the field of public safety, such as security monitoring of large-scale events and disaster emergency response. Through collaborative detection by drones, it can quickly monitor and assess the situation on-site, providing support for emergency decision-making.

[0141] Related products: Emergency drone monitoring system, real-time disaster site monitoring platform, public safety management system.

[0142] 4. Drone Logistics and Delivery Network

[0143] Application areas: In drone logistics and delivery networks, the technology of this invention can be applied to optimize the communication and sensing functions of drones, improve the collaborative scheduling capabilities of drones during the delivery process, and ensure the safe flight of drones in complex environments.

[0144] Related products: drone logistics and delivery system, intelligent drone dispatch platform, drone communication and navigation system.

[0145] Evidence related to the technical effects obtained by the embodiments of the present invention.

[0146] Data simulations were conducted based on the proposed algorithm. The algorithm was analyzed under different sensing beam gain limitations and flight times to verify its effectiveness. At the same time, it was compared with other basic algorithms to demonstrate the algorithm's advantages in improving communication and sensing performance.

[0147] In the simulation process of this invention, it is assumed that the UAV's flight altitude remains constant throughout the entire flight cycle, and the initial position of the UAV is q. I =[20,20,100], the final position is q I = [300, 300, 100]. Path loss is set to β0 = -60dB. Noise power is set to... The maximum speed of the drones was set to 0.5 m / s, and our detection area was 600×600, with four target drones located within the target area. The specific simulation process is as follows:

[0148] By using software simulation, the performance of the present invention and traditional methods under different scenarios is simulated, and the technical effects are compared and analyzed. Figure 6 Simulation results compare the performance of the proposed algorithm with other basic algorithms at achievable communication rates under different sensing beam gain constraints. The implementations of other basic algorithms are as follows: the beamforming algorithm indicates that the UAV's trajectory is not optimized throughout the iteration process, maintaining straight-line flight; only the beamforming vector is optimized. The joint optimization algorithm jointly optimizes the UAV's trajectory and beamforming vector. Figure 6 As we can see, the algorithm proposed in this invention outperforms other algorithms in terms of achievable communication rate. With the increase of sensing beam gain, the achievable communication rate continuously decreases, indicating a trade-off between sensing and communication functions. By jointly optimizing the UAV's trajectory and beamforming vector, and leveraging the gain from the EKF sensing data fusion algorithm, the system's communication and sensing performance can be improved.

[0149] Figure 7 Simulation graphs show the performance of the proposed algorithm at achievable communication rates under different flight times. From... Figure 7 As can be seen, for the same flight altitude, the longer the flight time, the higher the achievable communication rate. Furthermore, when the drone's flight time is longer, its flight path is closer to the communication user, thus improving the system's communication performance while maintaining the same sensing performance.

[0150] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0151] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A collaborative detection method in an integrated sensor network for unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: Step 1: Construct an integrated UAV sensing system, in which base stations and detection UAVs jointly perform detection tasks; In a collaborative detection scenario, a model for the transmission and reception of communication and sensing signals by unmanned aerial vehicles (UAVs) is established. Step 2: Based on the maximum likelihood criterion, the detection sets of the base station and the UAV are fused using the EKF algorithm to perform perception data fusion; Step 3: Mathematically model the problem of maximizing the achievable communication rate; Step four: Enhance communication performance by jointly optimizing UAV trajectory and beamforming algorithms to solve the optimization problem of maximizing achievable communication rate; The specific operation steps of the EKF sensing data fusion algorithm are as follows: A1. Initialize the empty set S F ; A2. Calculate the normalized Euclidean distance Ψ between the two sets of detection results. i,j ; A3. Traverse the two sets of detection results, Γ d For the set threshold value, if Ψ i,j ≤Γ d Perform sensor data fusion and put it into set S F Otherwise, directly put it into set S. F ; The specific steps of data fusion are as follows: A31, State Prediction: x n|n-1 =g(x n-1 )+ω n ; A32. Calculation of the predicted covariance matrix: A33. Kalman filter gain calculation: A34, Status Update: x n =x n|n-1 +K n (y n -h(x n|n-1 )); Where, x n Represents the state variable, y n Let g(x) represent the measurement variable, g(x) be the state transition function, h(x) be the Jacobian matrix of the measurement function with respect to the state variable, and n∈[1,2,…,N] represent the nth time slot.

2. The cooperative detection method in an integrated sensor network for unmanned aerial vehicles as described in claim 1, characterized in that, The drone uses integrated sensing and communication multi-beam signals, utilizing fixed sub-beams to perform communication functions and scanning sub-beams to perform sensing functions.

3. The collaborative detection method in an integrated sensor network for unmanned aerial vehicles as described in claim 1, characterized in that, The detection drone connects to a ground-based cellular network and flies from an initial point to a destination within a given time. During this time, the detection drone maintains reliable communication with the cellular network while simultaneously monitoring K intruding drones in the vicinity. The detection drone always flies at a fixed altitude H, and its flight time is t∈[0,T], divided into N = T / Δt equal-length time slots, each with a length of Δt. n∈[1,2,…,N] represents the nth time slot. Each time slot is set sufficiently small so that the detection drone's position remains unchanged within that time slot, and the detection drone's flight trajectory is q. u,c [n] = [x] u,c [n],y u,c [n],H] T ,1≤n≤N.

4. The cooperative detection method in an integrated sensor network for unmanned aerial vehicles as described in claim 1, characterized in that, The detection drone's transmission signal is Where s i [n] represents the transmitted information and w i [n] represents the corresponding beamforming vector; due to the limited energy carried by the detection UAV, its transmit power is limited to... The received signal is Where h c [n] represents the channel vector between the probe drone and the base station, z c [n] is the variance, which is... The additive white Gaussian noise, where the first term represents the desired received communication signal and the second term represents channel interference; The signal-to-interference-plus-noise ratio (SINR) of the received communication signal is The achievable communication rate for receiving communication signals is C. com [n] = log2(1+γ) c [n]); The transmit beam gain of the sensing function is expressed as: Where, q u,c [n] is used to detect the flight path of the drone. Represents the guiding vector, θ n Indicates the angle of arrival.

5. The cooperative detection method in an integrated sensor network for unmanned aerial vehicles as described in claim 1, characterized in that, The detection results from the drone and the base station are respectively represented as S. U With S B Where S = (Ω, V), Let V represent location information, and V = μ represent movement state information. The normalized Euclidean distance between the two sets of detection results is expressed as: in as well as These represent the maximum Euclidean distance between the location information and the movement status information, respectively.

6. The cooperative detection method in an integrated sensor network for unmanned aerial vehicles as described in claim 1, characterized in that, The drone needs to start from point q within the specified time. I Fly to the finish line q F The maximum flight speed is V max The maximum travel distance between the two time slots is D. m =V max Δt; Flight limit is q u,c [1] = q I q u,c [N] = q F , and ||q u,c [n+1]-q u,c [n]||≤D m .

7. The cooperative detection method in an integrated sensor network for unmanned aerial vehicles as described in claim 1, characterized in that, The joint optimization of UAV trajectory and beamforming algorithms includes the following steps: A1. Initialize the beamforming vector matrix W and the UAV flight trajectory Q, and set the iteration number l = 0; A2. For a given UAV flight trajectory Q, the original optimization problem is transformed into an optimization problem with only the beamforming vector matrix W as the optimization variable. By using the positive semidefinite relaxation technique, the non-convex problem is transformed into a convex optimization problem, and the optimal beamforming vector matrix W can be solved using a convex optimization toolkit. l+1 ; A3. For a given beamforming vector matrix W l+1 The original optimization problem is transformed into an optimization problem with only the UAV flight trajectory Q as the optimization variable. By using the continuous convex approximation technique, the non-convex problem is transformed into a convex optimization problem, and the optimal power allocation matrix Q can be solved using the convex optimization toolkit. l+1 ; A4. Using the already obtained beamforming vector matrix W l+1 and drone flight trajectory Q l+1 Find the maximum achievable communication rate in the current iteration. make γ0 is the set threshold value. If γ≥γ0, update the iteration count l=l+1 and return to step A2; otherwise, end directly.

8. A cooperative detection system in a UAV sensor network, comprising a cooperative detection method for a UAV sensor network as described in any one of claims 1 to 7, characterized in that, This includes detecting drones and base stations; The base station and the detection drone jointly perform the detection task; In a collaborative detection scenario, a model for the transmission and reception of communication and sensing signals by unmanned aerial vehicles (UAVs) is established. Based on the maximum likelihood criterion, the EKF algorithm is used to fuse the sensing data of the base station and the UAV detection sets; Mathematical modeling of the problem of maximizing achievable communication rates; By jointly optimizing UAV trajectories and beamforming algorithms, communication performance is enhanced, solving the optimization problem of maximizing achievable communication rates.

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